The continual increase in the cost of energy has triggered individuals to become more cognizant of the energy consumed in a residence. Power companies provide energy consumption data to individuals in a monthly billing statement. However, that energy consumption data gives a monthly snapshot of the energy consumption and fails to provide detailed energy consumption data as to the energy consumed by the individual on a moment by moment basis or even a daily basis. Such a monthly snapshot is difficult for an individual to parse through and analyze to determine which type of actions can be taken by the individual to reduce their energy consumption.
Rather than waiting for the monthly billing statement to arrive to determine how energy consumption can be reduced, energy consumption data provided to the individual in real-time, historical data, or data representative of other users enables the individual to instantly make decisions to reduce energy consumption. Energy consumption data in time intervals shorter than a month provides the individual with the type of feedback necessary for the individual to assess their energy usage in relatively short intervals of time, as well as historical data of their energy usage, and/or data representative of other users' energy consumption and then execute informed decisions to reduce that energy consumption instantly while not experiencing a significant sacrifice in doing the reduction. For example, an individual assesses that a significant energy consumption spike occurs when they turn on a humidifier. The individual then in real-time determines to run the humidifier at reduced periods of time rather than waiting until the monthly billing statement arrives to determine that a significant increase in energy consumption occurred that month. The individual may recognize their friend, who owns the same device and agreed to share data and compete, has reduced their energy use by a greater value than themselves.
Conventionally, energy consumption data is generated from sensors that monitor the magnetic field generated by current as it flows through each individual branch circuit conductor positioned in a circuit breaker panel. For example, a sensor could be positioned to monitor a branch circuit conductor that supplies current to a washer and dryer. The monitored current is then converted to energy consumption data for each individual branch circuit conductor. However, the energy consumption data is not representative of the overall energy consumption in a residence, and/or of individual devices within the residence, but rather the energy consumption of each individual branch circuit conductor. As a result, the individual would still be required to parse through the energy consumption data of each individual branch circuit conductor to assess their overall energy consumption. Further, other types of conventional energy monitoring devices require installation inside some type of electrical box which significantly increases the risk of electrocution if the user were to install themselves and/or require additional fees for an electrician to install rather than the user.
The current flowing through each of the branch circuit conductors generates significant magnetic fields that may skew the energy consumption data of each branch circuit conductor. For example, a sensor that monitors a branch circuit conductor that supplies current to a washer and dryer may capture portions of the magnetic field from a branch circuit conductor that supplies current to the furnace. As a result, the energy consumption data calculated based on the magnetic field generated by the current that is supplied by the washer and dryer may also be influenced by the magnetic field generated by the current that is supplied to the furnace, thus skewing the energy consumption data for the washer and dryer. Inaccurate energy consumption data prevents the individual from executing decisions to reduce their energy consumption.